randd Archives - rinf.tech https://www.rinf.tech/tag/randd/ Wed, 26 Jun 2024 11:14:11 +0000 en-US hourly 1 https://www.rinf.tech/wp-content/uploads/2020/05/favicon-150x150.png randd Archives - rinf.tech https://www.rinf.tech/tag/randd/ 32 32 Fostering Innovation: R&D’s Role in Embedded Engineering https://www.rinf.tech/fostering-innovation-rds-role-in-embedded-engineering/ Wed, 26 Jun 2024 11:07:34 +0000 https://www.rinf.tech/?p=27992 Exploring the pivotal role of R&D in driving innovation and digital transformation within embedded engineering. 

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Fostering Innovation: R&D's Role in Embedded Engineering

In today's rapidly evolving technological landscape, embedded engineering is a pillar of innovation, driving the expansion of interconnected devices across industries and domains such as healthcare, automotive, smart infrastructure and others.

Embedded systems have become omnipresent, powering devices ranging from smart home appliances to complex industrial machinery. The volume of connected IoT devices has not just grown; it has skyrocketed. According to recent data, there are approximately 14.76 billion connected IoT devices as of today. Thanks to 5G and other technologies, this figure is expected to almost double to 25.44 billion total IoT devices by 2030. Embedded systems power these IoT devices and enable seamless communication, automation, and data processing capabilities. From smart thermostats that optimize energy consumption to industrial sensors that monitor manufacturing processes, the applications of embedded engineering continue to expand, enhancing efficiency, safety, and productivity across various sectors. 

At the heart of embedded engineering progress lies Research and Development (R&D). R&D serves as the innovation engine, pushing the boundaries of what is possible through experimentation, prototyping, and rigorous testing. It empowers engineers to explore novel ideas, address emerging challenges, and capitalize on opportunities presented by technological advancements. By investing in R&D, companies stay competitive and take charge of shaping the future of embedded systems. 

R&D is not just about exploring new technologies and materials, but it’s about addressing emerging challenges such as security vulnerabilities and environmental robustness, underlining the crucial need for continuous innovation to meet and exceed market demands.  

This article delves into the pivotal role of R&D in driving innovation and digital transformation within embedded engineering. 

Contents

R&D's Impact on Embedded Engineering Innovation

R&D, as the pioneering force, is driving transformative advancements in embedded engineering, reshaping the landscape of interconnected technologies. Through continuously exploring new technologies, pushing the boundaries of performance, and addressing emerging challenges, R&D initiatives are redefining what embedded systems can achieve across various industries, inspiring a future of endless possibilities.  

Exploring New Technologies 

R&D is vital in driving innovation within embedded engineering by leading the discovery and integration of cutting-edge technologies. The global market for embedded systems is projected to grow significantly, from $121.2 billion in 2023 to $176.36 billion in 2031, fueled by increasing demand across healthcare, automotive, and consumer electronics sectors.  

R&D efforts are instrumental in bringing to fruition the advanced hardware components, such as high-performance processors and miniaturized sensors, which are crucial for enhancing the capabilities and efficiency of embedded systems. Moreover, R&D initiatives focus on pioneering complex software solutions, including real-time operating systems (RTOS) and efficient communication protocols, essential for enabling seamless connectivity and data processing in IoT applications.  

As IoT applications become more common, the demand for efficient and secure device management solutions is escalating. Industries ranging from manufacturing to healthcare and automotive increasingly rely on IoT devices to enhance operational efficiency, improve product quality, and foster innovation. Thus, robust R&D efforts are needed to design solutions that ensure seamless integration, secure communication, and efficient management of multiple interconnected devices. The challenge lies in developing embedded systems that meet the performance requirements, adhere to strict security standards, and provide ease of use for various stakeholders. 

At rinf.tech, we built an advanced zero-touch IoT device onboarding solution that leverages embedded systems’ power to revolutionize how manufacturing plants connect and manage their IoT devices. The solution integrates various technologies, including OpenCV, OpenVINO, MQTT, and AMQP, to provide a reliable and secure system for connecting devices to enterprise networks effortlessly. The embedded system architecture at the heart of the solution enables the automation of the onboarding process, ensuring seamless and secure connectivity without manual intervention. This improves safety and operational efficiency and enhances security by protecting the IoT devices and the enterprise network from potential threats and vulnerabilities.

By integrating embedded systems into the solution, we created a robust and efficient onboarding system that streamlines the process, improves data accuracy, and drives business growth. 

Pushing Performance Boundaries 

Continuous R&D investments drive significant improvements in embedded systems’ performance metrics. These efforts require enhanced power efficiency, increased processing speeds, and optimized memory utilization, which are essential for meeting modern applications’ rigorous demands.  

In sectors like industrial automation and logistics, where reliability and operational efficiency are paramount, R&D innovations enable devices to operate with greater precision and responsiveness. For instance, one of our implementations for a logistics client involved deploying an advanced inventory management tracker system. This innovative solution leverages embedded systems’ power to revolutionize how hospitals and healthcare facilities manage their consignment inventory items. At the heart of this solution is a custom-designed smart tracker device built using STM32 microcontrollers and S2-LP and Quectel NB-IoT transceivers, enabling real-time monitoring of consignment item usage. The device’s embedded system architecture allows it to send periodic updates to a central management platform, providing valuable insights into consumption patterns and enabling data-driven decision-making. The embedded system’s low power consumption and long battery life ensure the device can operate reliably for extended periods without requiring maintenance.  

By integrating embedded systems into the solution, we created a robust and efficient inventory management system that improves visibility, reduces waste, and optimizes inventory levels, ultimately driving cost savings and enhancing the overall efficiency of healthcare facilities. 

Furthermore, ongoing research into new materials and design techniques facilitates the development of smaller, more durable embedded systems capable of withstanding harsh environmental conditions. This focus on performance enhancement enhances user experience and drives competitiveness in a crowded marketplace. 

Addressing Emerging Challenges 

As embedded systems become increasingly connected and integrated into our daily lives, the need to address security vulnerabilities and privacy concerns has become paramount. R&D efforts in this space are crucial to staying ahead of evolving threats and ensuring the safety and privacy of end-users. Embedded systems, which power a wide range of devices from smart home appliances to industrial automation equipment, are often seen as potential entry points for cyber-attacks. Malicious actors may attempt to exploit vulnerabilities in the software or hardware of these systems to gain unauthorized access, disrupt operations, or steal sensitive data.   

R&D efforts are focused on developing privacy-preserving data management strategies, such as data anonymization, secure data storage, and user-centric consent mechanisms, to ensure that sensitive information is handled responsibly and complies with evolving data protection regulations. 

Another key challenge facing embedded systems is the issue of electromagnetic interference (EMI) and ensuring system robustness in harsh environments. Embedded devices are often deployed in environments with high levels of electromagnetic noise, such as industrial settings or areas with dense wireless communication infrastructure. This can lead to signal disruptions, data errors, and system failures. R&D teams are working to develop advanced shielding techniques, noise-cancellation algorithms, and robust circuit designs to mitigate the impact of EMI on embedded systems. This includes using specialized materials, innovative circuit topologies, and advanced signal processing algorithms to ensure that embedded devices can operate reliably and consistently, even in the most challenging environments. 

Furthermore, embedded systems are often required to function in harsh conditions, such as extreme temperatures, high humidity, or exposure to vibrations and shocks. R&D efforts are focused on enhancing these systems’ overall robustness through rugged hardware components, advanced thermal management solutions, and complex fault-tolerance mechanisms. This ensures that embedded devices can operate reliably and deliver their intended functionality, even in demanding environmental conditions. 

R&D Strategies for Fostering Innovation

Effective R&D strategies are essential in steering the innovation path within embedded engineering. By cultivating a culture of creativity, harnessing advanced tools and techniques, and staying attuned to market dynamics, R&D teams drive technological advancements and shape the future of interconnected technologies.  

Cultivating a Culture of Innovation 

Fostering a culture of innovation within R&D teams is imperative for driving continuous progress in embedded engineering. Studies indicate that companies with solid innovation cultures are more likely to outperform their competitors in terms of revenue growth and market share. Cultivating an environment that encourages experimentation and creative problem-solving is foundational. By empowering team members to explore unconventional ideas and approaches, R&D teams can uncover breakthrough innovations that propel the development of next-generation embedded systems. Furthermore, fostering collaboration with universities and research institutions enriches R&D endeavors by integrating academic expertise and diverse perspectives, encouraging a synergistic approach to innovation. 

Using Advanced Tools and Techniques 

Integrating advanced tools and techniques is a game-changer in enhancing R&D capabilities for embedded systems. According to recent reports, the global simulation software market is experiencing rapid growth, driven by increased demand for virtual prototyping and the adoption of Industry 4.0 technologies. The market size is valued at $17.87 billion in 2024 and is expected to grow to $61.96 billion by 2033. Simulation tools are indispensable assets, allowing R&D teams to conduct virtual testing and simulate real-world scenarios before physical implementation. This reduces time-to-market and minimizes costs associated with iterative design processes. Prototyping platforms facilitate rapid iteration and validation of concepts, enabling agile development of embedded solutions tailored to diverse industry needs.  

Machine learning algorithms are increasingly integrated into R&D frameworks, enhancing predictive analytics capabilities and optimizing system performance.  

Moreover, the role of data analytics cannot be overstated, as it enables R&D teams to glean actionable insights from vast datasets, guiding informed decision-making and uncovering novel research avenues. Together, these advanced tools and techniques empower R&D efforts in embedded engineering to innovate efficiently and effectively, ensuring solutions are not only cutting-edge but also robust and scalable for future applications. 

Staying Ahead of the Curve 

Being proactive in monitoring industry trends and technological advancements is not just a suggestion, but a necessity for staying competitive in embedded engineering. Studies have demonstrated that companies that prioritize continuous market research and user needs analysis are better positioned to anticipate shifts in consumer preferences and emerging technologies. This proactive approach is more than just a strategy. Still, it is a lifeline, allowing organizations to align their R&D efforts with evolving market dynamics, ensuring that innovations in embedded systems are not only cutting-edge but also relevant and responsive to industry demands. By staying ahead of trends like artificial intelligence, quantum computing, and sustainable materials, companies can strategically position themselves as leaders in the ever-evolving landscape of embedded engineering. 

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The Future of R&D for Embedded Engineering

The future of R&D in embedded engineering is set for change with the advent of emerging technologies like AI. By 2028, the global market for AI in embedded systems is projected to reach $18 billion, driven by applications in robotics, autonomous vehicles, and smart home devices. AI promises to revolutionize R&D methodologies by enabling predictive maintenance, operational efficiency, and real-time decision-making capabilities within embedded systems.  

Quantum computing is expected to play a pivotal role in the future of embedded engineering R&D, with its potential to exponentially increase processing speeds and computational power. This advancement opens the door to more complex embedded applications across industries. As these technologies mature, R&D efforts will increasingly focus on integrating AI and quantum computing, predicting a new era in embedded systems’ performance, scalability, and intelligence. 

The landscape of R&D in embedded engineering is shifting towards a more collaborative and open-source approach. Industry reports are increasingly pointing to a growing inclination towards collaborative research models, where companies, research institutions, and open-source communities pool their resources and expertise to accelerate innovation. This collaborative approach not only fosters knowledge sharing and reduces development costs but also promotes the standardization of technologies across the embedded systems ecosystem. Open-source initiatives like Arduino and Raspberry Pi have played a pivotal role in democratizing access to development platforms and nurturing a dynamic community of developers, driving innovation with embedded technologies. In the future, we expect that R&D in embedded engineering will increasingly leverage these collaborative and open-source frameworks to drive wider adoption of advanced solutions and collectively address complex challenges. 

A solid commitment to sustainability and environmental oversight also marks the future of embedded engineering R&D. As industries worldwide strive towards carbon neutrality and sustainable practices, embedded systems are crucial in optimizing resource usage and minimizing environmental impact. There is a growing demand for eco-friendly materials and energy-efficient designs in embedded systems, driven by regulatory pressures and consumer preferences for sustainable products. R&D efforts are thus focusing on developing energy-efficient processors, recyclable materials, and lifecycle management strategies that align with sustainable development goals. By placing sustainability at the forefront of R&D practices, companies can meet regulatory requirements, enhance brand reputation, and positively contribute to global environmental goals. 

Conclusion

R&D is the cornerstone of embedded engineering innovation, driving continuous advancements that redefine technological possibilities. R&D efforts are instrumental in exploring new technologies, pushing the boundaries of performance, and addressing emerging challenges such as cybersecurity and environmental sustainability. By fostering a culture of innovation and leveraging advanced tools and techniques, R&D teams enhance embedded systems’ capabilities and anticipate future trends and market demands.  

Looking ahead, the future of R&D in embedded engineering holds promise. The integration of AI, quantum computing, and sustainable practices is poised to revolutionize how embedded systems operate and interact within diverse applications.  

Companies that prioritize R&D as a strategic requirement are well-positioned to lead an increasingly interconnected world were innovation drives progress and competitiveness. Embracing R&D’s transformative potential ensures that embedded engineering continues to evolve, meeting the changing needs of industries and society. 

For companies looking to harness the power of R&D for custom embedded software engineering projects, rinf. tech’s expertise in embedded R&D can accelerate your innovation journey and bring cutting-edge solutions to market. Leveraging deep hardware and software development expertise, our initiatives span sectors like IoT, automotive, and industrial automation. Through collaborative partnerships and a commitment to innovation, we drive the development of cutting-edge embedded software and hardware solutions that enhance operational efficiency, reliability, and scalability.   

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]]> How to Roll Out an Innovation Project Within Your Company: Tips and Best Practices https://www.rinf.tech/how-to-roll-out-an-innovation-project-within-your-company-tips-and-best-practices/ Mon, 05 Feb 2024 10:40:59 +0000 https://www.rinf.tech/?p=17756 This article will share three strategies to help R&D teams effectively and successfully implement new technologies.

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How to Roll Out an Innovation Project Within Your Company: Tips and Best Practices

Innovation has always been a critical component of any organization's business success. But what is often less clear is how to translate new innovative ideas into practical implementation.

Sharing six strategies to help R&D teams effectively and successfully turn their innovative projects into full-fledged technology solutions accepted company-wide.

Contents

How to Transform an R&D Project into a Widely Accepted Technology Solution Across an Organization

To explore this question, Harvard Business Review analyzed several data sources from a major multinational oil company. It found that when the company adopted the cutting-edge technology that grew out of its R&D efforts, drilling costs were reduced by 15%, resulting in an average savings of $90 million a year per subsidiary. In an era of low oil prices, it brought about significant savings. 

However, despite this significant investment in innovation (as well as a clear indication that innovation pays off financially), HBR found that the company sometimes struggled to gain internal support for new technologies developed by its central R&D departments.

Our own analysis identified several common barriers and six strategies that have been particularly effective in encouraging the adoption of new tools and systems.

Here we go.

Strategies for Launching an Innovation R&D Project

1. Always Begin by Considering the Needs of the End User

Beginning with the end user in mind is a pivotal strategy for ensuring the successful implementation of innovative technologies within an organization. The common practice among many companies is to adopt a top-down approach, where new technologies are developed based on overarching strategic and financial goals, and then distributed across various departments and teams. While this method can sometimes yield results, it often encounters resistance from those on the front lines, such as engineers responsible for carrying out the implementation. These individuals may be hesitant to adopt directives from an R&D team located elsewhere, especially if the new technology has not yet proven its practical value in their specific work context.

A senior engineer highlighted this sentiment in an interview with HBR, noting, “Our operations departments don’t need new technology unless it’s proven to be absolutely useful.” This perspective is understandable, considering the high stakes involved with innovation projects. The risks and uncertainties associated with introducing new technologies can be significant, posing the potential to squander millions of dollars if the initiative fails.

However, adopting a bottom-up approach can effectively address these concerns. For instance, HBR recounted the experience of an engineer whose initial skepticism was overturned upon discovering a new system designed to mitigate water seepage in oil wells. This innovation, encountered during a visit to the headquarters, not only caught his interest but also led to its adoption despite initial resistance from his technology head. The remarkable success in reducing water seepage demonstrated the system’s value, convincing even the skeptical head of technology of its worth across the subsidiary.

The success of this innovation was not due to it being a simple “plug and play” solution. Instead, it required engineers to conduct extensive testing and modifications to tailor the technology to their specific operational needs. This example underscores the importance of engaging with the actual users of the technology from the outset. By directly involving those who will interact with the new system in their daily tasks, R&D teams can gain valuable insights into practical requirements, potential challenges, and the real-world impact of their innovations.

Convincing a few key individuals of the technology’s benefits can serve as a litmus test for its broader applicability. Although not every innovation will prove successful in every context, gaining traction within one segment of the organization can significantly bolster the case for a wider rollout. Therefore, it is crucial for R&D teams to not only focus on the technological aspects of their projects but also to invest time in understanding and addressing the needs, concerns, and realities of their end users. This user-centric approach can dramatically increase the likelihood of successful adoption and implementation across the entire organization, ensuring that innovative projects translate into tangible improvements and efficiencies.

2. Get Early Adopters Onboard

Securing the support of early adopters is a critical next step after initially focusing on end-user needs. To effectively drive the adoption of new technologies, R&D teams must identify which segments within the organization are most likely to embrace these innovations. Often, the best candidates are those who have faced persistent challenges or limitations with current systems and are therefore more open to exploring new solutions. For instance, engineers who have grappled with issues like water seepage for an extended period may be more inclined to welcome novel technologies that promise to address such problems.

Another approach involves targeting individuals or teams with a vested interest in the technology being developed. For example, when an R&D team is working on a digital upgrade for an essential analog control system, engaging with the operations team responsible for the original system can be highly beneficial. These teams are likely to possess deep insights into the system’s strengths and weaknesses, making them well-equipped to appreciate the advantages of the new digital solution.

The Harvard Business Review case study underscores the effectiveness of two strategic initiatives aimed at fostering closer ties between R&D teams and field engineers. Firstly, by identifying engineers with prior R&D experience, companies can create a bridge between current development efforts and practical field applications. Keeping these engineers informed about the latest technological advancements ensures that valuable insights from the field are incorporated into the R&D process, enhancing the relevance and applicability of new solutions.

Secondly, the establishment of R&D centers that facilitate movement of researchers across different divisions represents a forward-thinking approach to innovation. These centers serve as hubs of technological advancement, where R&D personnel can immerse themselves in the diverse operational contexts of the company. By working closely with field engineers in their natural environments, researchers can gain a firsthand understanding of specific challenges and requirements, enabling them to tailor their inventions more effectively to meet the needs of various teams.

Both strategies highlight the importance of creating a dynamic, interactive relationship between those developing new technologies and their eventual users. By doing so, R&D teams can ensure that their innovations are not only technically sound but also practically viable and directly responsive to the unique demands of their organization. Engaging early adopters in this way not only facilitates smoother implementation and integration of new technologies but also fosters a culture of innovation and collaboration throughout the company.

3. Navigate Financial Challenges

Navigating financial hurdles is often the final but significant obstacle that teams encounter when attempting to develop and deploy new systems. The dual requirements of software engineering expertise and considerable financial resources can appear daunting. However, these challenges are not necessarily prohibitive.

A wealth of inventive strategies exists for securing the necessary resources, whether through grants, external funding, or leveraging an internal incubator program. In the case of addressing water seepage, for instance, the R&D team successfully negotiated support by convincing an internal venture fund to finance the preliminary testing phase. This not only provided the essential capital but also demonstrated organizational faith in the project’s potential.

Furthermore, adopting a flexible approach to project management can offer a solution to financial and risk-related concerns. Our research indicates a natural hesitation among large project managers to introduce unproven technologies due to the perceived risk. An effective strategy to overcome this involves identifying smaller, less risky projects as initial testing grounds for new technologies. This allows for a proof of concept to be established with minimal financial risk. Once proven on a smaller scale, these innovations can then be more confidently rolled out to larger projects, equipped with tangible evidence of their effectiveness.

This strategic alignment of projects of varying scales creates a symbiotic relationship between different initiatives within the organization. Large projects benefit from minimized risk and the opportunity to implement proven technologies, while smaller projects gain access to cutting-edge solutions that would typically be beyond their budgetary reach. This not only mitigates potential financial losses but also fosters an environment of innovation and experimentation across the board.

It’s imperative for R&D departments to recognize that they may not always have the capacity to bear the full cost of introducing new technologies. Thus, exploring these creative financing solutions becomes essential. By doing so, they can engage and empower local champions within the organization who are key to the successful adoption and scaling of innovative technologies. These strategies not only circumvent the financial obstacle but also reinforce a culture of innovation, collaboration, and strategic risk-taking that can drive the organization forward.

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4. Foster Cross-Departmental Collaboration

Encouraging collaboration between different departments and teams can significantly enhance the adoption of new technologies. By fostering a culture of cross-departmental teamwork, R&D teams can leverage diverse perspectives, expertise, and insights, which can lead to more effective and universally accepted solutions. This strategy involves creating interdisciplinary teams that include members from R&D, operations, marketing, finance, and any other relevant department to ensure that the development and implementation of the technology consider all aspects of the business. Cross-departmental collaboration not only enriches the development process with a broader range of insights but also facilitates smoother integration of the technology into various areas of the organization, thereby enhancing overall buy-in.

5. Implement Agile Development and Iterative Feedback

Adopting an agile development approach can also play a crucial role in successfully rolling out new technologies. Agile methodologies prioritize flexibility, continuous improvement, and rapid iterations based on user feedback. By involving end-users and stakeholders in the development process through regular reviews and adjustments, R&D teams can ensure that the technology evolves in a way that closely aligns with user needs and organizational goals. This iterative process allows for the early identification and resolution of potential issues, making the final product more likely to be accepted and adopted across the organization. Additionally, this approach helps to build a sense of ownership and acceptance among users, as they see their feedback and suggestions being actively incorporated into the technology.

Both cross-departmental collaboration and agile development with iterative feedback are essential in bridging the gap between R&D projects and their successful adoption as technology solutions within an organization. These strategies complement the initial focus on end-user needs, engagement of early adopters, and overcoming financial obstacles by ensuring that the innovation process is inclusive, responsive, and aligned with the broader organizational context. By integrating these approaches into the innovation strategy, companies can significantly increase the likelihood that their R&D initiatives will result in impactful, widely accepted technological advancements.

6. Partner with a Technologically Advanced R&D Center

Seeking a partnership with an external R&D center that boasts advanced technological capabilities and expertise can offer another strategic avenue for accelerating the development and adoption of new technologies within an organization. This collaboration allows companies to tap into cutting-edge research, specialized knowledge, and innovative methodologies that may not be available in-house. By leveraging the resources and expertise of a tech partner, organizations can not only speed up the R&D process but also mitigate some of the financial and technical risks associated with developing new technologies from scratch.

A robust tech partner can provide access to a wide array of technologies, pilot projects, and prototypes, enabling an organization to explore and experiment with new solutions without the full commitment of internal resources. Furthermore, such a partnership can enhance the credibility of the technology being developed, making it easier to gain buy-in from internal stakeholders and early adopters. It also allows for the sharing of best practices, lessons learned, and market insights, which can refine the development process and ensure the technology is aligned with industry trends and customer expectations.

Incorporating a partnership with an external R&D center into the innovation strategy broadens the scope of possibilities for an organization, providing access to a richer pool of resources and expertise. This strategic move complements the internal efforts of engaging users, fostering cross-departmental collaboration, implementing agile development practices, and overcoming financial challenges. Together, these strategies form a comprehensive approach to transforming R&D projects into successful, widely accepted technology solutions across an organization, driving forward innovation and maintaining a competitive edge in the market.

An example of innovative solutions we build in our R&D Embedded Center

Wrapping up

In conclusion, transforming an R&D project into a technology solution that is embraced organization-wide involves a multifaceted approach that begins with a deep understanding of end-user needs and extends to strategic partnerships and agile development methodologies. The strategies outlined collectively form a robust framework for innovation.

The journey from R&D to widespread technological adoption within an organization is complex and fraught with challenges, from resistance to change among front-line users to financial and operational barriers. However, by adopting a user-centric approach, engaging early adopters, creatively overcoming financial hurdles, encouraging collaboration across departments, iterating based on feedback, and leveraging external expertise, organizations can significantly increase the odds of success. These strategies not only ensure that new technologies are developed with a clear understanding of real-world applications but also foster an environment where innovation is nurtured, supported, and effectively integrated into daily operations.

The case study from the Harvard Business Review and the strategies derived from it underscore the importance of a holistic and adaptable approach to innovation. By viewing each R&D project through the lens of these strategic pillars, organizations can navigate the complexities of technological adoption, ensuring that innovative solutions not only see the light of day but also deliver tangible benefits across the entire organization. Ultimately, the goal is to transform innovative ideas into practical, effective solutions that drive organizational success, enhance efficiency, and maintain a competitive edge in an ever-evolving technological landscape.

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]]> How to Improve Production Lines Control and Assembly Through Digital Twin Solutions https://www.rinf.tech/how-to-improve-production-lines-control-and-assembly-through-digital-twin-solutions/ Tue, 10 Oct 2023 12:40:32 +0000 https://www.rinf.tech/?p=25497 Guiding manufacturers on how to implement digital twin technology, fostering an environment where production is not only streamlined but also more intelligent and responsive to market demands.

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How to Improve Production Lines Control and Assembly Through Digital Twin Solutions

Recent technological advancements have forever transformed the manufacturing landscape. Today, factories benefit from various tools that mirror, monitor, and manipulate physical systems. The aim is to establish cyber-physical systems by connecting production assets that enhance the safety, quality, and efficiency of manufacturing processes.

Historically, the Industrial Revolution primarily influenced the autonomy of production systems. Recent technological advancements in factory automation systems have made them more intelligent, flexible, and less reliant on human intervention.

Manufacturers can also use “Digital Twins,” which are virtual representations of physical systems that constantly exchange information. This trendsetter is helping industries achieve efficiency, precision, and optimized processes. As such, digital twins in manufacturing open the door to unprecedented advancements and innovation.

In this article, we will delve deeper into how Digital Twin technology can be effectively utilized to improve control over production lines and assembly processes. We’ll explore its various applications, highlighting specific examples where the integration of Digital Twins has revolutionized manufacturing workflows, substantially increasing the precision, efficiency, and innovation in the production environment. The article will guide manufacturers on implementing this cutting-edge technology to reap maximum benefits, fostering an environment where production is not only streamlined but also more intelligent and responsive to market demands.

Contents

What is a Digital Twin?

Coined by Michael Grieves, Digital Twin describes a digital replica of a physical system. In manufacturing, digital twin refers to a dynamic software model of a production process or product that uses real-time data to mirror its physical counterpart.

Research forecasts that the global digital twin market will be worth $110.1 billion by 2028, growing at a Compound Annual Growth Rate (CAGR) of 61.3% from 2023 to 2028. The goal of digital twin solutions is to use these virtual counterparts for a variety of purposes like simulation, analysis, and control.

Benefits of Using Digital Twins to Improve Production Line Control and Assembly

The benefits of digital twin solutions in manufacturing are manifold. So, it’s unsurprising that it’s one of the most adopted technologies in the last decade. By simulating and monitoring systems, manufacturers are now able to access features that aren’t visible in the physical world.

Real-time Monitoring and Control

Digital twins offer a real-time view of the production line. Whenever this is the case, factory management can make immediate adjustments and exert complete control over the whole process.

Predictive Maintenance

Digital Twins come with the benefit of predictive analytics. This helps manufacturers identify potential machine failures or maintenance needs before they disrupt production. For example, product teams can test various operational scenarios and identify possible disruptions before making a decision.

Optimized Operations

Simulations are one of the best ways to predict the most likely outcome of any given scenario. Forecasting the results of process changes enables better decision-making. It can also help avert potentially disastrous scenarios.

Reduced Costs

Enhanced efficiency and predictive maintenance help minimize downtime and resource waste. Whenever all this comes together, it can positively impact the bottom line.

Examples of How Digital Twins are Being Used in Manufacturing Today

Several manufacturers are already harnessing the power of digital twins. For instance, in the automotive production line, a digital twin can enhance every step of the production process. Furthermore, digital twin also plays a critical role in maintenance protocols. In the aerospace industry, digital twins are employed to simulate and test aircraft performance under various conditions.

Another intriguing example is the integration of a Business Process Management or BPM engine with digital twins. Developing a custom BPM engine as a digital twin helps streamline business processes in real-time, optimizing the production line flow.

How digital twin improves plant design and operational performance
How to Use Digital Twins to Improve Plant Design and Operational Performance In Manufacturing

How to Implement a Digital Twin Solution for Production Line Control and Assembly

Step 1: Identify the Specific Aspects of Your Production Line for Enhancement

Understanding potential weak points in the production line and areas that need optimization is important. To do this, manufacturers must conduct an in-depth production process review. In this scenario, it’s not enough to just look at the data. You must also engage the production managers, line workers, and other stakeholders to get an in-depth understanding. This approach will help prioritize areas where efficiency gains, cost savings, or quality improvements can be realized.

Step 2: Collect Data from the Production Line

Get a holistic view of your operations by collecting factory and operations data from a variety of sources, such as sensors, machines, and software systems. It’s important to install sensors on critical equipment and machinery (if not already present) and integrate data collection modules with a BPM engine, Manufacturing Execution System (MES), and Enterprise Resource Planning (ERP) solution. Ensure that the data you capture is high fidelity, frequent, and accurate. The more granular the data, the better the insights one can derive from it.

Step 3: Craft a Digital Model of Your Production Line

Build a virtual representation that mirrors the physical production line. You can build this model using a variety of tools and software platforms. Still, choosing the right digital twin software platform is crucial. You should make a choice that is based on compatibility, scalability, and specific requirements. Once deployed, map out all production processes, machinery, and worker roles. Validate the digital model by cross-referencing it with physical processes to ensure accuracy.

Step 4: Real-Time Data Integration with the Digital Model

The primary objective is to build a live, dynamic digital twin that evolves seamlessly with the production line. Leverage the Internet of Things (IoT) devices or protocols to establish communication between sensors and the digital twin platform. In this scenario, ensuring that data transmission is secure and latency is minimal is critical. Furthermore, organizations must make an effort to calibrate and validate the sensors regularly to ensure data accuracy and effectiveness of the Digital Twin.

Step 5: Leverage the Digital Twin for Production Optimization

Finally, improve production efficiency, quality, and cost-effectiveness by leveraging insights from the digital twin. Manufacturers can do this by running simulations on the digital twin to predict potential inefficiencies, bottlenecks, or failures.

Factories can also use the insights from the digital twin to develop strategies for line balancing, reducing downtime, and improving overall throughput. Manufacturers can refine the digital twin’s accuracy and subsequent strategies by continuously comparing simulated results with actual production outcomes. However, to get the most out of digital twins, management must encourage and nurture a culture of iterative learning, leveraging Digital Twin as a tool for continuous improvement.

Looking to build a cost-efficient Business Process Management (BPM) engine for Production Lines ​Control & Assembly as a Digital Twin solution?

Check Out Our Value Proposition

Build a Cost-Effective BPM Engine

Implementing a robust BPM engine with a digital twin application in manufacturing requires a systematic approach. You have to have a clear understanding of the objectives to effectively integrate technology and data.

Process Compliance

Define, control, and enforce a process flow on the factory floor. Create a digital representation of objects, models, control, decision-making, and data collection. Develop products following a strict step order with control exemptions. Track models in relationship to the different objects involved and route the logic based on the business object state. You must always ensure observability over execution history and correlation.

State Transitions

A digital twin for any production line should be able to track material consumption, inventory movement through the production line, station state data collection, and accurately validate properties and data for processes. Execute API orchestration of processes when receiving material after the workflow is triggered.

Once factory teams properly execute the digital twin, production lines can become more agile, efficient, and responsive to the dynamic needs of modern manufacturing.

automation solutions for manufacturing
How Digital Twins Empower Manufacturing

Wrapping Up

In conclusion, Digital Twin technology is a cornerstone in modern manufacturing, offering an unparalleled approach to enhancing production line control and assembly. By creating virtual representations of physical systems, manufacturers can monitor, analyze, and optimize their processes in real time, achieving increased efficiency, precision, and cost-effectiveness.

Adopting Digital Twins allows for proactive maintenance, intelligent decision-making, and a significant reduction in resource waste and downtime, positively impacting the bottom line.

As we’ve seen from various real-world applications, including the automotive and aerospace industries, Digital Twins are versatile and invaluable tools for improving every step of the production process and maintenance protocols. Digital Twin technology integration with Business Process Management engines further streamlines and optimizes production line flow, leading to more agile and responsive manufacturing environments.

To successfully implement and leverage Digital Twins for production line enhancement, manufacturers must undertake a systematic and well-informed approach. This begins with identifying areas of improvement, collecting detailed and accurate data, crafting precise digital models, integrating real-time data, and continuously using the insights derived for production optimization. Importantly, fostering a culture of continuous improvement and iterative learning among factory teams is essential to fully harness the benefits offered by Digital Twin technology.

Through a thorough understanding and strategic application of Digital Twins, manufacturers are well-equipped to navigate the complexities of today’s production demands, ensuring the viability and thriving success of their operations in the competitive market landscape. The future of manufacturing is here, and it is digital, intelligent, and efficient.

Why build your digital twin with rinf.tech?

Domains & Solutions

  • Machine Learning, Deep Learning
  • Virtualization & Cloud
  • Smart Conf Room – Sentiment Analysis
  • IoT Devices & Protocols
  • Wireless Modules
  • BLE Bluetooth Low Energy
  • Device Efficiency
  • Secured Connectivity
  • ARM processors
  • Augmented & Mixed Reality
  • Modified kernels for FaaS
  • Long and Short Range Connectivity
  • M2M Applications

Tools & Frameworks

  • Docker, Kubernetes, MicroServices, FaaS, AWS, Azure, OpenStack, OpenVINO, OpenCV, CUDA
  • AR Core, AR Kit, Vuforia
  • Unity, WebGL
  • Sensors, Actuators

Protocols & Standards

  • NBIoT, LTE, UMTS, GSM, LoRaWan
  • SCRUM, SAFe

At rinf.tech, we have a full-fledged R&D Embedded Business Unit that specialises in custom software product engineering and proof-of-concept (PoC) project development. From computer vision powered intruder detection software to robotic arm and its digital twin to sophisticated deep learning models, we use experimental approaches and lessons learned to build top-notch solutions and prototypes to future-proof business ideas and emerging tech.

Learn More
Contact us

Looking for a technology partner?

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AI-driven workflow automation is transforming how organizations operate. By replacing time-consuming manual tasks with intelligent, self-optimizing systems, businesses can boost productivity by as much as 40% while reducing costs and minimizing errors. From enhancing customer service and streamlining software development to enabling data-driven decision-making, AI is not only modernizing traditional processes but also paving the way for a more agile and competitive future. This article delves into the evolution from rigid robotic process automation to adaptive AI workflows, offering practical insights on harnessing this technology to drive innovation and sustainable growth.

March 3, 2025

The post How to Improve Production Lines Control and Assembly Through Digital Twin Solutions appeared first on rinf.tech.

]]> Digital Twin Development: Why, When, and How? https://www.rinf.tech/digital-twin-development-why-when-and-how/ Thu, 02 Mar 2023 10:10:15 +0000 https://www.rinf.tech/?p=14059 This article explores why and when businesses should consider building custom digital twins and what to take into account before jumping on the digital twin development project.

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Digital Twin Development: Why, When, and How?

This article explores why & when businesses should consider building custom digital twins and what they need to take into account before jumping on the digital twin development project.

Contents

What is a digital twin?

The Digital Twin Consortium defines a digital twin as “a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity”.

The digital twin technology uses design, engineering, geospatial and operational data to represent a physical object/facility and associated systems. The digital twin uses IoT-enabled sensors to collect and process asset data used in machine learning (ML) models. 

In addition to physical assets, digital twin technology can be used to replicate processes to collect data to predict their performance, remote control physical objects, and more.

What sets the digital twin apart from the standard 3D model is that it is linked to a real-time data stream, which enables it to evolve along with its real-life counterpart.

This means that it can offer an accurate analysis of what is happening in real time and check future performance and assess potential risks. This creates new opportunities for efficiency gains, cost reduction, processes streamlining, and more.

To better understand digital twin technology adoption level, let’s look at its current market stats and future forecasts.

Digital Twin market growth drivers & challenges in 2023

According to Grand View Research, the global digital twin market was valued at $11.12 billion in 2022 , and it is expected to grow at a CAGR of 37.5% from 2023 to 2030.

The COVID-19 pandemic caused supply chain disruptions and production halts, which resulted in the suspension of various activities along the value chain in industries like manufacturing, aerospace, and automotive. This had a negative impact on the digital twin market during the initial stages of the pandemic in 2020. Nevertheless, as the number of COVID-19 cases decreased and restrictions were lifted, the digital twin market started recovering strongly as industries increasingly adopted automation and virtualization of products and processes.

By combining digital twin technology with advanced technologies like artificial intelligence, cloud computing, and IoT, the market growth is projected to increase. AI and IoT technologies are being implemented by businesses to gather and analyze behavioral data from connected products and existing IoT devices, which can then be utilized to replicate the usage and performance of the current device in the digital twin model.

This enables product engineers and designers to monitor product performance, identify any issues, and anticipate future iterations of common problems. Additionally, the adoption of these technologies helps organizations enhance operations and system productivity, leading to improved overall product performance.

 

According to Research & Markets, the key factors driving the adoption of digital twins in organizations today are:

  • The evolution of artificial intelligence (AI) technologies, data science and the industrial Internet of Things (IIoT),
  • The rapid development of digital health and manufacturing solutions,
  • Increased demand for predictive maintenance and real-time monitoring data,
  • The need to reduce time to market and improve decision making,
  • The need for production optimization and cost savings, etc.

 

Speaking of the constraints holding back the massive adoption of digital twin technology, data security due to the widespread use of IoT and Cloud platforms is one of the main ones.

With the proliferation of cyberattacks on critical Cloud infrastructures over the past decade, cybersecurity has become a major concern for industrial automation users and vendors. As such, the growing threat to the security of data connected to the Cloud is expected to be a major impediment to the growth of the digital twin market.

Another major challenge on the path to efficiently designing and deploying digital twins is the lack of awareness of the cost-benefit of implementing digital twins.

Despite the barriers, 31% of companies surveyed by Gartner in 2021 said they were already using digital twins to improve the safety of their employees or customers by implementing remote asset monitoring, control, and tracking. 

Types of digital twins

There are three main types of digital twins:

Product twins

Digital twins that are used to develop new products.

Manufacturing twins

Digital twins that are used to verify manufacturing processes.

Performance twins

Digital twins that are used for data collection, analysis, and informed decision making.

The combination and integration of these three types of digital twins is known as the digital thread. This can be incorporated into other products by collecting data from all product life cycle stages and production.

For a long time, creating digital twins was costly. Companies needed to hire full-time developers and engineers and invest in sophisticated software. Testing and data visualization were limited. Then, mixed reality technology and wearable gadgets came to the fore and changed the game by simplifying digital twin creation.

The benefits of a digital twin differ depending on when and where it is used. For example, using digital twins to monitor existing products, such as a wind turbine or oil pipeline, can reduce maintenance costs and save many millions in associated costs. 

Digital twins can also be used to create prototypes before production, reducing product defects and shortening time to market.

Common benefits of digital twins include:

  • System reliability, availability and overall performance improvement through real-time monitoring and simulation. 
  • Reduction of the risk of accidents and unplanned downtime due to failure.
  • Maintenance costs reduction by predicting failure before it occurs and ensuring that scheduled maintenance and repair works won’t affect production goals. 
  • Continual improvements by analyzing custom patterns and ensuring product quality through real-time performance testing.

 

Digital twins can use augmented reality (AR) and gaming technologies (e.g. Unity) – a.k.a. Mixed Reality – to train driver assistance systems with synthetic sensor data. A detailed walkthrough of each scenario helps validate safety requirements and build vehicles that can respond correctly without the driver being present.

Now let’s have a closer look at digital twin use cases across various industries and domains.

Digital twin use cases in different domains

Digital twins in manufacturing

Manufacturers use digital twins to optimize everything from end-to-end supply chain to operations, quality management, and custom manufacturing. Testing multiple solutions before actually building products helps determine the best cost, service, and capacity options. Organizations can minimize the impact of disruptions by identifying the best secondary source of supply.

Boeing used digital twins to predict the performance of various components. This resulted in 40% better quality the first time around, saving development time and money. The company used IoT sensors to achieve the perfect load balance, ensuring that every flight delivers the perfect amount of cargo.

With digital twins, manufacturing companies can improve reliability and efficiency. They can constantly learn and help avoid future problems.

Digital twins in healthcare

The medical sector has benefited from the digital twin technology in organ donation, surgical training and procedure risk reduction. Digital twin systems also mimic the flow of people through hospitals and track where infections may exist and who may be at risk of contact.

The Dassault Systèmes team has created a virtual human heart to develop new medical devices and analyze drug safety. Researchers at Linköping University Hospital have created digital twins of diseases, including breast cancer and influenza, to improve diagnosis and treatment.

 

Digital twins in real estate

According to EY, the adoption of digital twins can reduce real estate operating costs by up to 35%, drive down carbon emissions, deliver a healthier workplace, and enhance user experiences.

According to TechWire Asia, of all real estate companies that already use custom digital twins:

  • 50% report increase of sustainability and resiliency,

  • 35% report increase of building maintenance and operational efficiency;

  • 30% admit increase in productivity, and

  • 15% admit better space utilization.

Check out the interview with Bogdan Popescu, our Technical Director of the R&D Embedded Business Unit

on HackerNoon

How to build digital twins?

Digital twins are complex solutions, so specialized companies usually build and manage them as part of their R&D and custom product development value proposition. Digital twin development companies take care of the entire lifecycle of the digital twin. They also provide custom onboard data collection platforms and applications, dashboards, and tools that harness the power of the digital twin tech.

Finding the right skills and experience

Your organization has probably already realized the value of data and digitization, but may not have the internal competence and capacity to fully exploit it. 

By outsourcing digital twin development and data processing and analytics to a technology solutions company, you can focus on the practical application of the insights they provide.

Understanding the digital twin life cycle will help you understand what capabilities you need from a custom digital twin software development company.

Digital Twin Architecture
digital twin architecture

Digital twin architecture by EY

Digital Twin toolkit development

The development phase includes building a toolkit that can be used to create unique digital twins for various assets. 

Toolkit development is the hardest part of the digital twin’s lifecycle. It is typically done in R&D by highly specialized data scientists, machine learning specialists and more.

Development work involves gathering information about the physical characteristics of the asset, the operating environment, and any conditions under which the asset is operated. In addition, the automatically-collected data from the asset is required to develop the required machine learning methodologies. The result is a mathematical model that can be used to model the behavior and performance of an asset.

 

Digital twin design

There are two main elements in developing a digital twin. First, you need to select the supporting technology required to integrate a physical asset into its digital twin to enable real-time data flow from IoT devices and integration with operating rooms.

You must have a clear understanding of what device and modeling software are needed to create a 3D representation of the asset, and who will have access to the information in the digital twin or gain control of the physical asset through it.

Managing IoT devices securely is critical to overcoming the risks associated with identifying devices on your network.

It provides capabilities to authenticate, provision, configure, monitor, and manage each device. An identity-based IoT platform enables you to do this quickly and securely at scale.

This leads to the second design element. You must understand what type of information is required throughout the life of an asset, where this information is stored, and how it can be accessed and used. The information must be structured and reusable so that it can be quickly and efficiently exchanged between systems.

An identity-based IoT platform can manage the identity of each item in the digital twin and provide messaging services to automate secure communication between those people, systems and objects.

 

Creating standalone digital twins

Standalone digital twins can be useful for monitoring key metrics or states of one specific asset, such as HVAC or environmental systems. They can also be used to match key building site elements with their original design.

A stand-alone digital twin usually contains a small number of data sources. At this point, you can create a twin enhancement toolkit using accurate “as is” data from other sources. For example, data from CAD systems, 3D models, or point clouds generated by laser scanning can be used to correlate with the sensor output.

 

Creating duplicated digital twins

Digital modeling of a duplicated digital twin can help prevent real problems. Smart components connected to the cloud can collect sensor data, allowing real-time status analysis and comparison with previous metrics.

The duplicate digital twin contains all the important and measurable data sources needed to represent the entire asset.
Standalobe digital twin elements can be combined to form a duplicate model. Here, the granularity of the model may be too detailed for your needs. Make sure your goals are clear so you don’t collect too much data.

Database-driven BIM models provide an excellent foundation for duplicating a model. BIM was developed to enhance collaboration in the construction industry. It also provides a single source of truth platform that allows flexible viewing of data. These capabilities will also help digital twin models deliver the right information to the right people at the right time. Software analytics and AI will update the digital twin as its physical counterpart changes.

Harnessing the potential of duplicated digital twins involves moving away from sensors and dashboards to advanced virtual representations of physical assets, with the risk of added complexity.

 

Creating enhanced digital twins

Digital twins can go far beyond individual pieces of equipment or individual buildings. The future of the digital twin is determined by scale.

The enhanced digital twin expands to include other data sources from the connected site. This will include correlated environmental data from external sources and information from external analytics and algorithms.

The more complex the system, the more difficult it is to manage and control all of its components. This is why digital twins are opening up tremendous opportunities for the interconnected cities of the future. They will provide the data and analysis you need to optimize people, planning and resources.

Final Thoughts

Digital twins have many benefits as they improve the behavior of processes and products, which usually means more efficient operations.

Digital twins allow us to anticipate potential problems in the future. This reduces product defects and, among other things, shortens production times.

Digital twins help improve and optimize production processes through real-time information. They help reduce unplanned downtime due to potential errors and reduce the number of accidents, allowing us to simulate all kinds of possible situations.

Digital twin technology helps reduce maintenance costs by performing preventive maintenance tasks. It provides opportunities for continual improvement through simulations and the detection of failures and inefficiencies.

The digital twin is a great Industry 4.0 tool you need to leverage to become a future-ready organization.

Why build your digital twin with rinf.tech?

R&D Embedded Systems

Domains & Solutions

  • Machine Learning, Deep Learning
  • Virtualization & Cloud
  • Smart Conf Room – Sentiment Analysis
  • IoT Devices & Protocols
  • Wireless Modules
  • BLE Bluetooth Low Energy
  • Device Efficiency
  • Secured Connectivity
  • ARM processors
  • Augmented & Mixed Reality
  • Modified kernels for FaaS
  • Long and Short Range Connectivity
  • M2M Applications

Tools & Frameworks

  • Docker, Kubernetes, MicroServices, FaaS, AWS, Azure, OpenStack, OpenVINO, OpenCV, CUDA, AR Core, AR Kit, Vuforia
  • Unity, WebGL
  • Sensors, Actuators

Protocols & Standards

  • NBIoT, LTE, UMTS, GSM, LoRaWan
  • SCRUM, SAFe

At Rinf.tech, we have a full-fledged R&D Embedded Business Unit that specialises in custom software product engineering and proof-of-concept (PoC) project development. From computer vision powered intruder detection software to robotic arm and its digital twin to sophisticated deep learning models, we use experimental approaches and lessons learned to build top-notch solutions and prototypes to future-proof business ideas and emerging tech.

Read More
Contact us
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Twitter
LinkedIn

Looking for a technology partner?

Let’s talk.

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]]> rinf.tech Obtains Research Tax Credit (CIR) for French Customers https://www.rinf.tech/rinf-tech-obtains-research-tax-credit-cir-for/ Tue, 11 Jan 2022 10:37:46 +0000 https://www.rinf.tech/?p=16644 rinf.tech reinforces its position as an Innovation Supplier in France by receiving a Research Tax Credit (Crédit d’impôt recherche (CIR)) accreditation, following CII accreditation.

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rinf.tech Obtains Research Tax Credit (l'agrément CIR) for French Customers

and Gets Official Accreditation as an R&D Supplier in France

rinf.tech reinforces its position as an Innovation & R&D Supplier in France by receiving a Research Tax Credit (l'agrément pour le crédit d'impôt recherche (CIR)) accreditation, following CII (l'agrément pour le crédit innovation) accreditation.

CIR provides R&D support for companies of all sizes and sectors, be it a high-tech startup or a large corporation opening a research and development center in France.

CIR covers applied research, basic research, and experimental software development. It acts as a deduction of tax due for the current fiscal year and for the period when the research was carried out.  The Research Tax Credit allows funding for innovation as well as patent protection for inventions.

The CIR rate depends on the amount invested:

 

  • 30% on R&D expenditures up to €100 million for innovation project

  • 20% for innovation activities of up to €400,000 (for SMEs)

  • For expenses exceeding the €100 million threshold, the deduction rate is 5%.

Who can benefit from CIR?

Any industrial, commercial, and agricultural enterprise, regardless of its legal status, that is based in France and is subject to corporate or income tax in the BIC category and is taxed on the basis of its actual profit.

What R&D expenses qualify as CIR?

  • Tech staff
  • Operating expenses
  • Outsourced R&D expenses
  • Depreciation costs, etc

 

Our existing and future French customers that develop their R&D projects with rinf.tech are eligible to apply to Research Tax Credit and get up to 30% of their expenses reimbursed by the French government.

In 2021, we obtained Innovation Tax Credit (l’agrément CII) allowing French businesses to get up to 20% of their software development expenses reimbursed.

Want to work together?

Let’s talk.

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]]> How to Capture Industrial IoT (IIoT) Value Through R&D Innovation https://www.rinf.tech/how-to-capture-industrial-iot-iiot-value-through-rd-innovation/ Wed, 17 Nov 2021 17:17:23 +0000 https://www.rinf.tech/?p=15503 This article focuses on the key technological changes enterprises need to make in order to support their innovation goals and how R&D can be an effective solution and a driver of IIoT success and sustainability.

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How to Capture Industrial IoT (IIoT) Value Through R&D Innovation

A guide for manufacturing enterprises looking to accelerate the pace of innovation

Innovations in the industrial software stack and applications for advanced analytics, artificial intelligence, machine learning, 5G connectivity, edge computing, and the Industrial Internet of Things (IIoT) are potentially valuable business assets.

However, for many manufacturing companies and businesses, "technical screening" can be the easy part. Capturing value and creating and scaling effective use cases is where the challenge lies.

Contents

Use cases need to be digitally supported, but heterogeneous systems and application landscapes can become obstacles due to outdated software and/or decentralized solutions and technologies. These technical problems, combined with unclear business plan hurdles and lack of organizational capacity, end up driving many companies into a constant pilot purgatory.

In recent years, there have been significant advances in technology, especially in scalable connectivity and integration, which finally allowed enterprises to upgrade and extend their existing solutions rather than fully replace them. From 5G to AI to edge computing and beyond – the emerging technologies enable companies to implement and scale efficient use cases with minimal additional cost when used wisely.

Over the years of creating and delivering high-performance, customized IoT solutions, we at rinf.tech have noticed that manufacturing companies with a holistic approach are the most successful ones. In particular, they benefit from the digitization of industry along the entire value chain, providing a range of advantages to both their suppliers and customers.

This article focuses on the key technological changes enterprises need to make in order to support their innovation goals and how R&D can be an effective driver of IIoT success and sustainability.

What does it take to deliver IIoT success?

Reaping the benefits of IIoT requires a holistic approach to drive the end-to-end transformation of an organization and technology.

To successfully develop IIoT projects, enterprises must focus on three core pillars:

  • IIoT Infrastructure: Plan and design an IIoT architecture to scale digital use cases across an organization.
  • Data Infrastructure: Get the right data at the right time and in the right quality for digital and analytics.
  • Technology ecosystem: Expanding a network of companies to collaborate with to bring innovation to the enterprise faster and more effectively.

 

So why is it essential for enterprises, especially manufacturing, to use IIoT and advanced technologies for digital transformation projects?

There are several good reasons why companies should start or continue to use IIoT and advanced technologies:

  • Deployment of scalable use cases is accelerating as platforms become more user-friendly. That is, from low-code to code-free software, toolkits make development and installation more cost-effective than ever.
  • Decentralized computing is becoming more popular as infrastructure solutions make it easy to manage distributed networks of platform resources and solve the problem in real-time.
  • Integration and connectivity are being dramatically improved by frameworks such as the unified architecture of Open Platform Communications (OPC) and the emergence of 5G, offering high-speed, secure and highly flexible low-latency solutions where existing alternatives don’t work.
  • Computing power and processing power have grown exponentially, while storage and CPU costs have plummeted.

 

The benefits of IIoT are significant

To name just a few:

  • Tremendous improvements in performance, productivity, resilience, flexibility, speed to market and customization can be achieved through proper IIoT implementation.
  • Moving to advanced manufacturing helps significantly increase the resilience of manufacturers, allowing them to respond faster to crises with modern digital work planning tools and solutions.

Successful implementation of IIoT at scale follows key actions in three areas:

  • On the business side

First, companies need to identify, prioritize, and test new use cases. They then need to create an achievable roadmap for deploying these use cases across IT, OT, and all enterprise/plant locations. After that, it would be great to capture value and measure opportunities with overarching impact and without distracting attention to local requirements for solutions. However, in terms of continually improving local initiatives, they need to be monitored and continued.

  • On the company side

Organizations need to set clear targets for the entire digital transformation and create a team/unit responsible for monitoring progress and adjusting courses when needed. Then a new way of working should be introduced that fosters cross-functional communication and develops related skills and capabilities.

  • On the technology side

First, enterprises need to define the current situation and target architecture of their IIoT platform, focusing on collecting, connecting, ingesting, and integrating data in ways that enable innovative use cases, including platform cybersecurity management.

Second, you need to understand the impact of cloud computing and integrate it into the overall design of the platform.

Third, an ecosystem of suppliers and partners needs to be built to support implementation, taking into account the different complexity levels of individual enterprises. They include a type of production and products, enterprise size and IT-OT landscape, etc.

Where does R&D step in to make a difference?

R&D is essential for innovative IIoT use case development and future-proofing.

Research and development (R&D) is the part of a company that seeks knowledge to develop, design and improve its products, services, technologies or processes. Along with creating new products and adding features to old ones, R&D investments integrate different parts of a company’s strategy and business plans, such as marketing and cost reduction.

R&D consists of investigative actions that a person or business takes to obtain the desired discovery result. A completely new product, product category, or service will be created.

The development part refers to applying a new science or thinking so that a new and better product or service can begin to take shape.

Research and development is essentially the first step in developing a new product, but product development is not only research and development. As an offshoot of research and development, product development can refer to the entire product life cycle, from concept to sale, refurbishment, and retirement.

R&D for IIoT use case development

Deploying IIoT use cases is facilitated mainly by the following factors:

  • Enhanced usability of IoT platforms

IoT and IIoT platforms become more user-friendly as the software supports user extension. In addition, as access to IIoT software development expands, more experimentation will take place to benefit the entire IIoT manufacturing ecosystem.

  • Simplified development

A rich set of ready-to-use APIs, code snippets and microservices, well-established communication standards, and available solutions with little or no coding ensure more cost-effective creation and implementation of use cases.

These low-code/no-code platforms offer small and midsize businesses access to IIoT software functionality without the need for talented programmers that early adopters should have had.

  • Imprinted scalability

Routines for rapidly provisioning and updating applications in reusable data pipelines, containerization for hardware-independent software deployment across multiple peripherals, and sophisticated solutions to manage the entire software/hardware stack enable DevSecOps methodologies to be implemented and help scale platforms rapidly across the enterprise.

  • 5G and AI become the catalysts of change when it comes to IIoT innovation

For enterprises migrating to Industry 4.0, 5G offers corresponding short-term IIoT opportunities. With these dramatic productivity gains, factories and plants can overcome the severe disruptions inherent in pre-Industry 4.0 production halls. As a result, manufacturing is expected to account for more than half of all 5G sales when 5G brings significant benefits.

Are you looking to leverage external R&D to future-proof your IoT solution's feasibility and/or build PoC fast and cost-effectively?

Get in Touch

Using R&D to harness the power of 5G, AI, Edge Computing, and Mixed Reality

5G

Some of the most compelling examples of 5G in manufacturing are automated guided vehicles and real-time process control. Today, automated vehicles typically connect to a Wi-Fi network and rely on software loaded on the vehicle to manage routing and task execution. 5G will dramatically improve connectivity to the edge of the cloud for real-time sharing data between vehicles and coordinating fleets, thereby allowing them to free themselves from fixed paths in an ever-changing manufacturing environment.

As an added benefit, 5G also improves reliability by offering seamless connectivity as automated vehicles move through the factory and switch between access points or radios. When it comes to real-time process control, wired controllers and sensors can be used for years. However, 5G opens up a new space for solutions, helping to connect modules for relatively easy targeted analysis of sensors and actuators – especially important for older machines, whose control and power systems may be outdated and less adaptable to modern technology.

Artificial Intelligent (AI)

Artificial intelligence represents the third era of computing, usually defined as the ability of machines to perform cognitive functions as or better than humans. These functions include perception, learning, reasoning, problem solving, contextual understanding, inference and prediction, and creativity.

The convergence of groundbreaking research, business use cases, skyrocketing data growth, and improvements in computing power and storage are all encouraging advances in AI.

As there’s no single standard marking a clear distinction between weak AI and strong AI, both AI researchers and AI project managers find it difficult to make the right decision about architectures and technologies that should be used to deliver innovative AI-driven experiences. R&D provides a solid platform for experimentation, which results in better justification of corporate AI spending.

In particular, R&D allows for extensive experimentation and discovery around the following AI areas:

  • MLOps
  • Low-code/no-code machine learning (ML)
  • Web-scale content analysis
  • Empathy and emotions simulation
  • Artificial Emotional Intelligence (AEI)
  • Serverless computing
  • Data synthesis 
  • Digital twins
  • Advanced AI chipsets, etc.

Edge Computing

AI-powered decision making and processing happens closer to the data source generation, in contrast to the cloud, a technique known as “edge computation”. Internet of things and its billions devices combined with 5G network and increased processing power, has made large-scale AI possible at the edge of the possible.

Data processing directly on devices will be important in the future for healthcare, automotive and manufacturing applications as it is potentially faster and safer.

 

In 2021, US tech giant Dell Technologies has officially launched a $66 million R&D center in Singapore to spur edge computing innovation.

Source: TNP Tweet

Mixed Reality (MR)

Microsoft designed HoloLens 2 head-up display (HUD) specifically for business solutions leveraging cloud and AI capabilities, interoperability with industry applications, and a suite of developer tools.

The device has already been adapted for the US Army and many of its advanced features include thermal imaging and night vision.

Smart glasses and headgear maker Nreal has announced an Android-compatible all-in-one headset for enterprises that looks more like a helmet than a pair of goggles, with built-in edge computing capabilities. These business-focused headsets are used in a wide variety of applications, from monitoring supply chains and complex equipment with digital twins, to conducting remote 3D meetings and providing augmented reality guides in on-the-job training.

These and other solutions have already motivated many R&D teams to experiment with AR, VR, Diminished Reality (DR) and integrate them with AI, machine learning, and Cloud.

Our rinf.tech R&D team is no exception. We’ve recently built a HoloLens-based digital twin of the robotic arm used for touchscreen testing.

Future IIoT architecture, as per McKinsey

future IoT architecture

How to start developing an IIoT R&D project

Step 1: Discover and deploy innovative value-driven use cases that deliver positive impact

Deploying early use cases is an investment, and therefore it is important to think about the deployment in a strategic manner. In most situations, it is recommended to deploy use cases based on value/feasibility.

First, select use cases that create immediate value and avoid use cases that require building databases from scratch or other complex prerequisites.

While the focus is usually on low-effort, high-value use cases, there is a strong case for using use cases with little or no ROI. For example, a use case may not result in immediate impact, but it may allow future use cases to be used.

This decision is pre-analyzed, and the rationale must be clearly stated so that all relevant stakeholders are aware of the investment and its value, even if that value is delayed.

Step 2. Begin collecting and aggregating data while building important features in parallel

Not all items need to be securely fastened before the deployment. In particular, even before the required data is connected, prioritization of use cases provides an overview of the required data sources and an understanding of what data is available and can be connected. The data collection architecture can be customized or defined for a specific OT equipment and data source based on this information.

However, in parallel, the development of the platform core should not be neglected and should start in sync with the development of the use cases. It is necessary to connect sources for automatic data collection as soon as possible. Depending on the implementation state of the target architecture, a use case can be deployed to a local server based on a specific architecture. This assumes the use of frameworks that will eventually be used on the back end.

But it won’t take long to implement the final architecture prioritizing the first use cases, and the migration of the first use cases will be possible in just a few weeks. Further, each use case is implemented in the target architecture. In preparation for this, consider and engage the next wave of use cases by connecting to the required data sources in the previous wave (s).

The point to keep in mind here is that many use cases take longer to prepare simply because some data sources do not yet exist. To compensate for this, it may be necessary to add sensors, change the PLC logic of the machine, update systems, and so on.

Step 3. Establish processes and collaboration that will ensure the required deployment

A standard process of connecting to new businesses should be established to ensure that the team is in place when deployments begin there. Implementation is also about changing how an organization works and thinks; thus, it is important to make informed change management the same priority as the deployment itself. The success of a use case can be undermined if the benefit has not been clearly identified and people are either not using a new application or system in their day-to-day work or are using it incorrectly.

A central IIoT deployment team takes care of the needs across the enterprise, making sure everything is in place prior to deployment. The list of needs includes the skills and the technical background required for a use case. The R&D team identifies the right people on-site to manage the deployment and resolves issues.  The central team also monitors deployment status and emerging issues.

This provides a holistic view of benefits, implementation status and problem solutions to generate best practices for IIoT innovation.

A deeper look at use cases will almost certainly reveal a need for skills that may not currently be available in an organization or individual business. Hiring new talent or training existing employees should begin as early as possible so that skills are in place when they are needed.

Skill development and capacity building is an ongoing effort because deploying use cases never ends. Investing in these areas is a must for an organization that wants to succeed in its Industry 4.0 efforts.

It is also important to have a clear collaboration model that identifies one person for each workstream with clear accountability. Effective communication must be ensured within the organization, such as the exchange of information between enterprises and between the organization and its partners. Important archetypes of value chain partners are industrial equipment suppliers, technology providers and integrators, as well as specialists in functional areas.

Employee training should begin before deployment (or even concurrently) to ensure a shared vision and understanding of each use case capabilities and advantages. Training sessions are critical as time is devoted to answering employee questions and concerns. In addition, getting the people who will be working on a new application or tool into the development process and getting their feedback can create a tool that better suits their needs and provides broader acceptance and interest.

It will also be important to define the expectations and responsibilities of the use case owners as they will define the subsequent sprints within the use cases and report status updates and issues. Consistency of processes is very important, such as how communication occurs and measuring status and progress.

 

R&D is an integral part of any IoT project, and expanding it can be the first step to project success. By leveraging research and development, your business can optimize costs, reduce risk, and minimize time-to-market for various IoT applications. R&D teams manage both the technical and business elements of the product. Their responsibilities include evaluating both the usefulness of the product and the technology itself.

In addition to this, R&D teams can see future business opportunities. The end result of this should be research, prototyping, and clarification on the IoT product. The R&D team can study the idea before it becomes a product and support the transformation of the concept into a functional asset that solves the user’s problems.

Benefits of utilizing R&D for IIoT projects

Fast time to POC, Prototype and MVP

IoT technology is evolving at a rapid pace. Why not save time and effort by testing ideas with a Proof of Concept (POC), Minimum Viable Product (MVP), or Prototype before launching a specific product?

POC is the practice of deep product interpretation. This happens before any planning for a product. POC allows you to learn a concept without spending too much time and money.

Prototype has the same basic goal of helping companies understand if a product is potentially good enough to get the job done. 

The R&D team can test the product’s performance, design, and benefits by creating a prototype. This way, you can use the prototype with a limited group of people to test it out before going to market for multiple users.

MVP is a product with a sufficient number of features that can be implemented in the market. The goal of MVP is to provide feedback for the future development of IoT projects. Choosing between one or all of these three can be a little tricky, and doing one of them requires specialists and some IoT equipment.

Enterprise innovation cost optimization

Digital transformation and innovation projects often lead to overhead costs. This includes administrative costs in excess of the total cost of the equipment. Using R&D for IIoT projects will help reduce these costs. Many tech companies provide their R&D services to businesses looking for a variety of outsourcing options. Some of these companies even have entire departments that focus on experimenting with cutting edge technology. They investigate and scrutinize IoT solutions, deep learning, robotics, digital twin and other technologies to create solutions, POCs and MVPs for themselves and their customers.

Better Control and Risk Management

The company must interpret risks and be able to assess or mitigate them immediately. Using R&D for IoT projects can help you mitigate these risks. R&D engineers need to understand well how new IoT devices or functions will work and whether they can meet your business goals and requirements.

Like any other technology, IoT solutions need deep research and development and are best done by professionals. R&D outsourcing allows you to optimize costs, get an idea to market faster and avoid life-saving risks.

Final Thoughts

Wrapping up, expanding market participation, cost management benefits, developing marketing capabilities and keeping up with trends are all reasons why companies invest in R&D. Research and development can help a company follow or stay ahead of market trends and keep the company up-to-date.

While R&D requires complex engineering-heavy resources, quality certifications, compliance with the industry standards and more, the innovations resulting from it may actually work to lower costs through more efficient manufacturing processes or more efficient and higher-quality products.

To accelerate enterprise innovation and create a unique and compelling IIoT value proposition, i’s highly recommended that manufacturing companies establish technology partnerships with companies that already have well-established R&D centers or innovation labs and that have accumulated best practices of leveraging the newest technologies and methodologies.

We'll share tips for choosing the right custom IoT software development partner in our next blog post. Meanwhile, check out what it takes to build a highly effective IoT development team in-house or offshore.

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The post How to Capture Industrial IoT (IIoT) Value Through R&D Innovation appeared first on rinf.tech.

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